Description Usage Arguments Details Value Author(s) References See Also Examples
Generate a random data matrix with or without proteomics, logtransformed feature intensitylike properties.
1 
model 
character indicating one of the three different type of models:

nrow 
number of rows of data matrix (only for 
ncol 
number of columns of data matrix
(only for 
show.fig 
logical inidicating whether data properties are plot to
figure (only for 
For model "rand"
, each matrix element is drawn from a
standard normal distribution N(0,1). For model "omics"
, the
matrix elements of each row are drawn from a Gaussian distribution
N(μ_i,σ_i^2) where the mean and standard deviation itself are
drawn Gaussian distributions, i.e. σ_i~N(0,0.0625) and
μ_i~N(28,4). About 35\
to the missing value pattern present in real protein LFQ
intensities. For model "omics.dep"
, a single differentially epxressed
RI feature is stacked on top of the matrix from model "omics"
.
matrix
of size nrow x ncol.
Ariane Schad
Brombacher, E., Schad, A., Kreutz, C. (2020). TailRobust Quantile Normalization. BioRxiv.
example_NApattern()
for description of missing value pattern.
1 2 3 4 5 6  mbqnSimuData(model = "rand")
mbqnSimuData(model = "rand", 2000,6)
set.seed(1234)
mbqnSimuData(model = "omics")
set.seed(1111)
mbqnSimuData(model = "omics.dep")

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